Data cleaning and auditing

library(httr)
library(jsonlite)
library(countrycode)
library(janitor)
library(readxl)
library(sf)
sf_use_s2(FALSE)
library(tmap)
tmap_mode('view')
library(tidyverse)
options(knitr.kable.NA = '')

Data cleaning

Read data

raw_metadata <- read_xlsx('data/draft/metada_work_version.xlsx', guess_max = 4000) 

Check columns

raw_metadata <- raw_metadata %>% 
  janitor::clean_names() %>% 
  janitor::remove_empty(c('rows', 'cols')) 

Check source fields

The fields are: name_orig, format, and language.

• Make sure there are no \r, \n, other weird characters or typos.
• Capitalise and clean language.

# name
raw_metadata %>% 
  filter(grepl('http', name_orig)) %>% 
  group_by(name_orig) %>% count

raw_metadata %>% 
  filter(is.na(name_orig)) %>% 
  distinct(url_clean)

raw_metadata %>% 
  mutate(name_orig = str_squish(name_orig)) %>% 
  mutate(name_orig = str_remove_all(name_orig, "\\\\")) %>% 
  mutate(name_orig = str_remove_all(name_orig, "\"")) %>% 
  group_by(name_orig) %>% 
  summarise(n_countries = n_distinct(country)) %>% 
  select(name_orig, n_countries) %>% 
  print(n=10)

# format
raw_metadata %>% 
  mutate(format = ifelse(format == 'NA', NA, str_squish(format))) %>% 
  distinct(format)

# language
raw_metadata %>% 
  mutate(language = ifelse(language == 'NA', NA, str_squish(language))) %>% 
  mutate(language = str_replace(language, "/|\\|", ' | ')) %>% 
  distinct(language) %>% 
  print(n=50)

DOUBTS

• There’s one source that has a URL instead of a name, that includes 12 records.

The name_orig is: https://www.odonat-grandest.fr/listes-rouges-grand-est-etat-avancement/.
The id values are: [1] 446 447 448 449 450 451 452 453 454 455 456 457.

• There are some formats with ? as value. What should they be?

FIX

# French: Liste rouge des Amphibiens du Grand Est
# English: Red list of Mammals of Grand Est    

raw_metadata %>% 
  mutate(name_orig = ifelse(grepl('htt', name_orig) & 
                               state_province == 'Grand Est',
                             str_glue('Red list of {group} of Grand Est'), name_orig)) %>% 
  mutate(name_orig = case_when(grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Birds' ~ 
                                 'Liste rouge des Oiseaux du Grand Est',
                               grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Mammals' ~ 
                                 'Liste rouge des XXX du Grand Est',
                               grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Orthoptera' ~ 
                                 '',
                               grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Fishes' ~ 
                                 '',
                               grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Butterflies' ~ 
                                 '',
                               grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Night butterflies' ~ 
                                 '',
                               grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Ladybugs' ~ 
                                 '',
                               grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Branchiopoda' ~ 
                                 '',
                               .default = name_orig))
  filter(state_province == 'Grand Est') %>% select(group, name_orig)

raw_metadata %>% 
  mutate(language = ifelse(language == 'NA', NA, str_squish(language))) %>% 
  mutate(language = str_replace(language, "/|\\|", ' | ')) %>% 
  distinct(language)

raw_metadata %>% 
  mutate(format = ifelse(format == '?', NA, format)) %>% 
  distinct(format)

raw_metadata %>% 
  mutate(name_orig = str_squish(name_orig)) %>% 
  mutate(name_orig = str_remove_all(name_orig, "\\\\")) %>% 
  mutate(name_orig = str_remove_all(name_orig, "\"")) 

Check Location fields

The fields are: continent, country, state_province, gadm_level_1, gadm_level_2, region_custom, region_detail, and iso_2.

• Make sure there are no typos, thus duplicates.
  
• Capitalise continent, country, state_province names.
  
• Check ISO codes.
  
• Check GADM levels.
  
• Clean region_custom and region_detail.

# check continent
raw_metadata %>% 
  mutate(continent = str_squish(str_replace_all(str_squish(continent), '\\|', ' | '))) %>%
  mutate(continent = str_replace_all(continent, '_', ' ')) %>%
  mutate(continent = str_to_title(continent)) %>% 
  distinct(continent)

# check country
raw_metadata %>% 
  mutate(country = ifelse(country == 'NA', NA, str_squish(country))) %>% 
  mutate(country = str_replace_all(country, '_', ' ')) %>%
  mutate(country = ifelse(country == 'USSR', NA, str_to_title(country))) %>%
  mutate(country = str_replace_all(country, 'And ', 'and ')) %>% 
  mutate(country = str_replace_all(country, 'Of', 'of')) %>% 
  mutate(country = str_replace_all(country, 'The', 'the')) %>% 
  distinct(country)

# check state_province
raw_metadata %>% 
  mutate(state_province = ifelse(state_province == 'NA', NA, str_squish(state_province))) %>%
  mutate(state_province = str_to_title(state_province)) %>% 
  mutate(state_province = str_replace_all(state_province, 'And ', 'and ')) %>% 
  mutate(state_province = str_replace_all(state_province, 'Of', 'of')) %>% 
  mutate(state_province = str_replace_all(state_province, 'The', 'the')) %>% 
  filter(!is.na(state_province)) %>% 
  filter(state_province != gadm_level_1) %>% 
  distinct(country, state_province, gadm_level_1, iso_2, iso_3) %>% 
  print(n=100)

# check gadm_level_1 and gadm_level_2
raw_metadata %>% 
  mutate(gadm_level_1 = ifelse(gadm_level_1 == 'NA', NA, str_squish(gadm_level_1))) %>%
  mutate(gadm_level_1 = str_to_title(gadm_level_1)) %>% 
  mutate(gadm_level_1 = str_replace_all(gadm_level_1, 'And ', 'and ')) %>% 
  mutate(gadm_level_1 = str_replace_all(gadm_level_1, 'Of', 'of')) %>% 
  mutate(gadm_level_1 = str_replace_all(gadm_level_1, 'The', 'the')) %>% 
  filter(!is.na(gadm_level_1)) %>% distinct(gadm_level_1) %>% 
  arrange(gadm_level_1) %>% print(n=100)

raw_metadata %>% 
  mutate(gadm_level_2 = ifelse(gadm_level_2 == 'NA', NA, str_squish(gadm_level_2))) %>%
  mutate(gadm_level_2 = str_to_title(gadm_level_2)) %>% 
  filter(!is.na(gadm_level_2)) %>% select(gadm_level_2)

# check region_custom and region_detal
raw_metadata %>% 
  mutate(region_custom = ifelse(region_custom == 'NA', NA, str_squish(region_custom))) %>% 
  mutate(region_detail = ifelse(region_detail == 'NA', NA, str_squish(region_detail))) %>% 
  mutate(region_detail = str_squish(str_replace_all(str_squish(region_detail), '\\|', ' | '))) %>%
  filter(!is.na(region_custom)) %>%
  distinct(region_custom, region_detail, iso_2, iso_3) %>% 
  print(n=100)

# check iso_2 and iso_3
raw_metadata %>% 
  janitor::clean_names() %>% 
  janitor::remove_empty(c('rows', 'cols')) %>% 
  mutate(iso_2 = ifelse(iso_2 == 'NA' & country != 'Namibia', NA, str_squish(iso_2))) %>% 
  mutate(iso_2 = str_squish(str_replace_all(str_squish(iso_2), '\\|', ' | '))) %>%
  mutate(iso_3 = ifelse(iso_3 == 'NA', NA, str_squish(iso_3))) %>% 
  mutate(iso_3 = str_squish(str_replace_all(str_squish(iso_3), '\\|', ' | '))) %>%
  select(country, iso_2, iso_3, region_custom, region_detail) %>%  
  filter(is.na(iso_2)) #%>% distinct()

DOUBTS

• What to do with country == USSR? Should be custom region and not country.
  
• Kosovo is iso_2 = XK? Yes, it’s a temporary, unofficial country code top-level domain for Kosovo.
  
• Is Antartica and French Souther Territories correct? Yes.
  
• Lots of state_province and GADM_level_1 to correct. Is GADM_1 in English? e.g., ‘Südtirols’ = ‘Bolzano’ (state_name in German?), ‘Centre-Val De Loire’ = ‘Centre’ (something missing?), ‘Corsica’ = ‘Corse’ (something missing?), ‘Flanders’ = ‘Vlaanderen’ (English?), ‘Opole Voivodeship’ = ‘Opole’ (something missing?).

Check Taxon fields

The fields are: kingdom, phylum, subphylum, class, order, and group.

• Check duplicates in all fields.
• Rename group to taxa.

I searched names using the GBIF backbone, using my own custom function nameMatcherGBIF().

# gbif name parser
nameMatcherGBIF <- function(sp_name_list) {
  
  # api <- 'http://api.gbif.org/v1/parser/name'
  api <- 'http://api.gbif.org/v1/species/match'
  
  name_parsed <- tibble(sp_name = character(),
                        scientificName = character(),
                        kingdom = character(),
                        phylum = character(),
                        class = character(),
                        order = character(),
                        family = character(),
                        genus = character(),
                        specificEpithet = character(),
                        species = character(),
                        status = character(), 
                        rank = character())
  
  for(sp_name in sp_name_list){
    # cat(sp_name, '\n')
    call_url <- str_glue('{api}?name={sp_name}&strict=true&verbose=false')
    get_json_call <- GET(url = URLencode(call_url)) %>%
      content(as = "text") %>% fromJSON(flatten = TRUE)
    
    if(get_json_call$matchType == 'NONE') {
      name_parsed_i <- tibble(sp_name = sp_name,
                              scientificName = NA,
                              kingdom = NA,
                              phylum = NA,
                              class = NA,
                              order = NA,
                              family = NA,
                              genus = NA,
                              specificEpithet = NA,
                              species = NA,
                              status = NA, 
                              rank = NA)
      name_parsed <- rbind(name_parsed, name_parsed_i)
    } else{
      name_parsed_i <- tibble(sp_name = sp_name,
                              scientificName = ifelse(exists('scientificName',get_json_call), get_json_call$scientificName, NA),
                              kingdom = ifelse(exists('kingdom',get_json_call), get_json_call$kingdom, NA),
                              phylum = ifelse(exists('phylum',get_json_call), get_json_call$phylum, NA),
                              class = ifelse(exists('class',get_json_call), get_json_call$class, NA),
                              order = ifelse(exists('order',get_json_call), get_json_call$order, NA),
                              family = ifelse(exists('family',get_json_call), get_json_call$family, NA),
                              genus = ifelse(exists('genus',get_json_call), get_json_call$genus, NA),
                              specificEpithet = ifelse(exists('specificEpithet',get_json_call), get_json_call$specificEpithet, NA),
                              species = ifelse(exists('species',get_json_call), get_json_call$species, NA),
                              status = ifelse(exists('status',get_json_call), get_json_call$status, NA), 
                              rank = ifelse(exists('rank',get_json_call), get_json_call$rank, NA))
      
      name_parsed <- rbind(name_parsed, name_parsed_i)
    }
  }
  return(name_parsed)
}

sp_list <- raw_metadata %>% 
  distinct(kingdom, phylum, subphylum, class, order, group) %>% 
  mutate(group = str_trim(group)) %>% 
  mutate(group = str_to_title(group)) %>% 
  mutate(group = str_replace_all(group, 'And ', 'and ')) %>% 
  mutate(group = str_replace_all(group, 'Of', 'of')) %>% 
  mutate(group = str_replace_all(group, 'The', 'the')) %>%
  pull(group)

sp_list_matched <- nameMatcherGBIF(sp_list) %>% suppressMessages()

sp_list_matched <- sp_list_matched %>% 
  mutate(scientificName = case_when(grepl('flora', sp_name, ignore.case=T) ~ 'Plantae',
                                    grepl('fauna', sp_name, ignore.case=T) ~ 'Animalia',
                                    grepl('tunicata', sp_name, ignore.case=T) ~ NA,
                                    .default = scientificName)) %>% 
  mutate(kingdom = case_when(grepl('flora', sp_name, ignore.case=T) ~ 'Plantae',
                             grepl('fauna', sp_name, ignore.case=T) ~ 'Animalia',
                             grepl('tunicata', sp_name, ignore.case=T) ~ NA,
                             .default = kingdom)) %>% 
  mutate(phylum = ifelse(phylum == 'chordata', 'Chordata', phylum))

sp_list_unmatched <- sp_list_matched %>% 
  filter(is.na(scientificName)) %>% pull(sp_name)

sp_list_matched %>% filter(!is.na(kingdom)) %>% nrow() # matched

[1] 205

length(sp_list_unmatched) # not matched

[1] 358

When the taxon name (i.e., group) was not found I kept the previous taxonomic fields’ values.

merged_list <- left_join(raw_metadata %>%
                           mutate(group = str_trim(group)) %>%
                           distinct(group) %>%
                           arrange(group),
                         sp_list_matched %>%
                           filter(!is.na(scientificName)) %>%
                           rename(group=sp_name) %>% distinct()) %>%
  arrange(group)

raw_metadata_taxon_list <- raw_metadata %>%
  mutate(group = str_trim(group)) %>% 
  distinct(group, .keep_all = T) %>% 
  select(kingdom, phylum, subphylum, class, order, group) %>% 
  arrange(group)

left_join(raw_metadata %>% mutate(group = str_trim(group)) %>%
            select(-c(kingdom,phylum,subphylum,class,order)),
          bind_rows(merged_list %>% 
                      filter(!is.na(scientificName)),
                    merged_list %>% 
                      filter(is.na(scientificName)) %>%
                      select(group) %>% 
                      left_join(. , raw_metadata_taxon_list))) %>% 
  mutate(group = str_trim(group)) %>% 
  mutate(group = str_to_title(group)) %>% 
  mutate(group = str_replace_all(group, 'And ', 'and ')) %>% 
  mutate(group = str_replace_all(group, 'Of', 'of')) %>% 
  mutate(group = str_replace_all(group, 'The', 'the')) %>%
  mutate(group = str_replace_all(group, 'Et Al.', 'et al.')) %>%
  distinct(group, kingdom, phylum, class, order, family, rank) %>% 
  arrange(kingdom, phylum, class, order) %>% 
  kableExtra::kbl(booktabs = T) %>% 
  kableExtra::kable_styling(latex_options = c('striped', 'hold_position'))

group	kingdom	phylum	class	order	family	rank
Earthworms	Animalia	Anelida	Clitellata	Opisthopora
Oligochaeta	Animalia	Anelida	Clitellata
Onychophora	Animalia	Animalia
Branchiobdellida	Animalia	Annelida	Clitellata	Branchiobdellida		ORDER
Hirudinea	Animalia	Annelida	Clitellata
Worms	Animalia	Annelida	Clitellata
Leech	Animalia	Annelida	Clitellata
Leeches	Animalia	Annelida	Clitellata
Echiurida	Animalia	Annelida	Echiura	Echiuroidea
Sipunculids	Animalia	Annelida	Sipuncula
Amblypygi	Animalia	Arthropoda	Arachnida	Amblypygi		ORDER
Spiders	Animalia	Arthropoda	Arachnida	Araneae
Uropodina	Animalia	Arthropoda	Arachnida	Mesostigmata
Opiliones	Animalia	Arthropoda	Arachnida	Opiliones		ORDER
Opilioness	Animalia	Arthropoda	Arachnida	Opiliones
Pseudoscorpiones	Animalia	Arthropoda	Arachnida	Pseudoscorpiones		ORDER
False Scorpions	Animalia	Arthropoda	Arachnida	Pseudoscorpions
Scorpions	Animalia	Arthropoda	Arachnida	Scorpionida
Arachnida	Animalia	Arthropoda	Arachnida			CLASS
Anostraca	Animalia	Arthropoda	Branchiopoda	Anostraca		ORDER
Branchiopoda	Animalia	Arthropoda	Branchiopoda			CLASS
Centipedes	Animalia	Arthropoda	Chilopoda
Chilopoda	Animalia	Arthropoda	Chilopoda			CLASS
Collembola	Animalia	Arthropoda	Collembola			CLASS
Entomostraca	Animalia	Arthropoda	Copepoda
Copepoda	Animalia	Arthropoda	Copepoda			CLASS
Millipedes	Animalia	Arthropoda	Diplopoda
Diplopoda	Animalia	Arthropoda	Diplopoda			CLASS
Archaeognatha	Animalia	Arthropoda	Insecta	Archaeognatha		ORDER
Blattodea	Animalia	Arthropoda	Insecta	Blattodea		ORDER
Wood Cockroaches	Animalia	Arthropoda	Insecta	Blattodea
Cockroaches	Animalia	Arthropoda	Insecta	Blattodea
Hydraenidae	Animalia	Arthropoda	Insecta	Coleoptera	Hydraenidae	FAMILY
Coleoptera	Animalia	Arthropoda	Insecta	Coleoptera		ORDER
Carabidae	Animalia	Arthropoda	Insecta	Coleoptera	Carabidae	FAMILY
Saproxylic Beetles	Animalia	Arthropoda	Insecta	Coleoptera
Beetles	Animalia	Arthropoda	Insecta	Coleoptera
Longhorn and Scarab Beetles	Animalia	Arthropoda	Insecta	Coleoptera
Longhorn Beetles	Animalia	Arthropoda	Insecta	Coleoptera
Scarabaeidae	Animalia	Arthropoda	Insecta	Coleoptera	Scarabaeidae	FAMILY
Ladybugs	Animalia	Arthropoda	Insecta	Coleoptera
Water Beetles	Animalia	Arthropoda	Insecta	Coleoptera
Tenebrionidae	Animalia	Arthropoda	Insecta	Coleoptera	Tenebrionidae	FAMILY
Soldier Beetles	Animalia	Arthropoda	Insecta	Coleoptera
Leaf Beetles	Animalia	Arthropoda	Insecta	Coleoptera
Histeridae	Animalia	Arthropoda	Insecta	Coleoptera	Histeridae	FAMILY
Sphaeritidae	Animalia	Arthropoda	Insecta	Coleoptera	Sphaeritidae	FAMILY
Derodontidoidea	Animalia	Arthropoda	Insecta	Coleoptera
Bostrichoidea	Animalia	Arthropoda	Insecta	Coleoptera
Staphylinidae	Animalia	Arthropoda	Insecta	Coleoptera	Staphylinidae	FAMILY
Lucanidae	Animalia	Arthropoda	Insecta	Coleoptera	Lucanidae	FAMILY
Geotrupidae	Animalia	Arthropoda	Insecta	Coleoptera	Geotrupidae	FAMILY
Trogidae	Animalia	Arthropoda	Insecta	Coleoptera	Trogidae	FAMILY
Silphidae	Animalia	Arthropoda	Insecta	Coleoptera	Silphidae	FAMILY
Chrysomelidae	Animalia	Arthropoda	Insecta	Coleoptera	Chrysomelidae	FAMILY
Bark Beetles	Animalia	Arthropoda	Insecta	Coleoptera
Ground Beetles	Animalia	Arthropoda	Insecta	Coleoptera
Curculionidae	Animalia	Arthropoda	Insecta	Coleoptera	Curculionidae	FAMILY
Powderpost Beetles	Animalia	Arthropoda	Insecta	Coleoptera
Bostrichidae	Animalia	Arthropoda	Insecta	Coleoptera	Bostrichidae	FAMILY
Anobiidae	Animalia	Arthropoda	Insecta	Coleoptera	Anobiidae	FAMILY
Ptinidae	Animalia	Arthropoda	Insecta	Coleoptera	Ptinidae	FAMILY
Deadwood Beetle	Animalia	Arthropoda	Insecta	Coleoptera
Buprestidae	Animalia	Arthropoda	Insecta	Coleoptera	Buprestidae	FAMILY
Snout Beetles	Animalia	Arthropoda	Insecta	Coleoptera
Staphylinoidea	Animalia	Arthropoda	Insecta	Coleoptera
Cucujoidea	Animalia	Arthropoda	Insecta	Coleoptera
Lamellicornia	Animalia	Arthropoda	Insecta	Coleoptera
Seed Beetles	Animalia	Arthropoda	Insecta	Coleoptera
Weevils	Animalia	Arthropoda	Insecta	Coleoptera
Anthribidae	Animalia	Arthropoda	Insecta	Coleoptera	Anthribidae	FAMILY
Platypodidae	Animalia	Arthropoda	Insecta	Coleoptera	Curculionidae	FAMILY
Ground Beetle	Animalia	Arthropoda	Insecta	Coleoptera
Tiger Beetles	Animalia	Arthropoda	Insecta	Coleoptera
Megalopodidae	Animalia	Arthropoda	Insecta	Coleoptera	Megalopodidae	FAMILY
Scarabaeoidea	Animalia	Arthropoda	Insecta	Coleoptera
Cerambycidae	Animalia	Arthropoda	Insecta	Coleoptera	Cerambycidae	FAMILY
Curculionoidea	Animalia	Arthropoda	Insecta	Coleoptera
Cleroidea	Animalia	Arthropoda	Insecta	Coleoptera
Elateridae	Animalia	Arthropoda	Insecta	Coleoptera	Elateridae	FAMILY
Lymexyloidea	Animalia	Arthropoda	Insecta	Coleoptera
Cicindelidae	Animalia	Arthropoda	Insecta	Coleoptera	Carabidae	FAMILY
Lucanoidea	Animalia	Arthropoda	Insecta	Coleoptera
Hydrophilidae	Animalia	Arthropoda	Insecta	Coleoptera	Hydrophilidae	FAMILY
Platypsyllinae	Animalia	Arthropoda	Insecta	Coleoptera
Cholevinae	Animalia	Arthropoda	Insecta	Coleoptera
Malachiidae	Animalia	Arthropoda	Insecta	Coleoptera	Malachiidae	FAMILY
Melyridae	Animalia	Arthropoda	Insecta	Coleoptera	Melyridae	FAMILY
Phloeophilidae	Animalia	Arthropoda	Insecta	Coleoptera	Phloiophilidae	FAMILY
Cleridae	Animalia	Arthropoda	Insecta	Coleoptera	Cleridae	FAMILY
Cerophytidae	Animalia	Arthropoda	Insecta	Coleoptera	Cerophytidae	FAMILY
Eucnemidae	Animalia	Arthropoda	Insecta	Coleoptera	Eucnemidae	FAMILY
Cryptophagidae	Animalia	Arthropoda	Insecta	Coleoptera	Cryptophagidae	FAMILY
Latridiidae	Animalia	Arthropoda	Insecta	Coleoptera	Latridiidae	FAMILY
Mycetophagidae	Animalia	Arthropoda	Insecta	Coleoptera	Mycetophagidae	FAMILY
Zopheridae	Animalia	Arthropoda	Insecta	Coleoptera	Zopheridae	FAMILY
Monotomidae	Animalia	Arthropoda	Insecta	Coleoptera	Monotomidae	FAMILY
Phalacridae	Animalia	Arthropoda	Insecta	Coleoptera	Phalacridae	FAMILY
Pyrochroide	Animalia	Arthropoda	Insecta	Coleoptera
Meloidae	Animalia	Arthropoda	Insecta	Coleoptera
Orsodacnidae	Animalia	Arthropoda	Insecta	Coleoptera	Orsodacnidae	FAMILY
Donaciinae	Animalia	Arthropoda	Insecta	Coleoptera
Leptinidae	Animalia	Arthropoda	Insecta	Coleoptera	Leiodidae	FAMILY
Lissomidae	Animalia	Arthropoda	Insecta	Coleoptera	Elateridae	FAMILY
Derodontoidae	Animalia	Arthropoda	Insecta	Coleoptera
Leiodidae	Animalia	Arthropoda	Insecta	Coleoptera	Leiodidae	FAMILY
Oedemeridae	Animalia	Arthropoda	Insecta	Coleoptera	Oedemeridae	FAMILY
Melandryidae	Animalia	Arthropoda	Insecta	Coleoptera	Melandryidae	FAMILY
Dung Beetles	Animalia	Arthropoda	Insecta	Coleoptera
Earwigs	Animalia	Arthropoda	Insecta	Dermaptera
Heteroptera	Animalia	Arthropoda	Insecta	Diptera	Sphaeroceridae	GENUS
Acalyptratae	Animalia	Arthropoda	Insecta	Diptera
Calypterate	Animalia	Arthropoda	Insecta	Diptera
Larger Brachycera	Animalia	Arthropoda	Insecta	Diptera
Dolichopodid	Animalia	Arthropoda	Insecta	Diptera
Lonchopteridae	Animalia	Arthropoda	Insecta	Diptera	Lonchopteridae	FAMILY
Platypezidae	Animalia	Arthropoda	Insecta	Diptera	Platypezidae	FAMILY
Opetiidae	Animalia	Arthropoda	Insecta	Diptera	Opetiidae	FAMILY
Hoverflies	Animalia	Arthropoda	Insecta	Diptera
Chaoboridae	Animalia	Arthropoda	Insecta	Diptera	Chaoboridae	FAMILY
Thaumaleidae	Animalia	Arthropoda	Insecta	Diptera	Thaumaleidae	FAMILY
Ceratopogonidae	Animalia	Arthropoda	Insecta	Diptera	Ceratopogonidae	FAMILY
Flies	Animalia	Arthropoda	Insecta	Diptera
Asilidae	Animalia	Arthropoda	Insecta	Diptera	Asilidae	FAMILY
Psychodidae	Animalia	Arthropoda	Insecta	Diptera	Psychodidae	FAMILY
Dixidae	Animalia	Arthropoda	Insecta	Diptera	Dixidae	FAMILY
Soldier Flies	Animalia	Arthropoda	Insecta	Diptera
Horse-Flies	Animalia	Arthropoda	Insecta	Diptera
Bee Flies	Animalia	Arthropoda	Insecta	Diptera
Empididae	Animalia	Arthropoda	Insecta	Diptera	Empididae	FAMILY
Conopidae	Animalia	Arthropoda	Insecta	Diptera	Conopidae	FAMILY
Chironomidae	Animalia	Arthropoda	Insecta	Diptera	Chironomidae	FAMILY
Dolichopodidae	Animalia	Arthropoda	Insecta	Diptera	Dolichopodidae	FAMILY
Black Flies	Animalia	Arthropoda	Insecta	Diptera
Long-Legged Flies	Animalia	Arthropoda	Insecta	Diptera
Micropezidae	Animalia	Arthropoda	Insecta	Diptera	Micropezidae	FAMILY
Grass Flies	Animalia	Arthropoda	Insecta	Diptera
Tachinidae	Animalia	Arthropoda	Insecta	Diptera	Tachinidae	FAMILY
Aquatic Empididae	Animalia	Arthropoda	Insecta	Diptera
Pediciidae	Animalia	Arthropoda	Insecta	Diptera	Pediciidae	FAMILY
Limoniidae	Animalia	Arthropoda	Insecta	Diptera	Limoniidae	FAMILY
Diptera	Animalia	Arthropoda	Insecta	Diptera
Parasitic Diptera	Animalia	Arthropoda	Insecta	Diptera
Mayflies	Animalia	Arthropoda	Insecta	Ephemeroptera
Auchenorrhyncha	Animalia	Arthropoda	Insecta	Hemiptera
Cicadas	Animalia	Arthropoda	Insecta	Hemiptera
Shieldbugs	Animalia	Arthropoda	Insecta	Hemiptera
Clavicornia	Animalia	Arthropoda	Insecta	Hemiptera	Aradidae	GENUS
Big-Eyed Bugs	Animalia	Arthropoda	Insecta	Hemiptera
Nepomorpha	Animalia	Arthropoda	Insecta	Hemiptera
Fulgoromorpha	Animalia	Arthropoda	Insecta	Hemiptera
Cicadomorpha	Animalia	Arthropoda	Insecta	Hemiptera	Palaeontinidae	GENUS
Scale Insect	Animalia	Arthropoda	Insecta	Hemiptera
Hemiptera	Animalia	Arthropoda	Insecta	Hemiptera		ORDER
Ants	Animalia	Arthropoda	Insecta	Hymenoptera
Bombus Spp.	Animalia	Arthropoda	Insecta	Hymenoptera
Hymenoptera	Animalia	Arthropoda	Insecta	Hymenoptera		ORDER
Bees	Animalia	Arthropoda	Insecta	Hymenoptera
Diversicornia	Animalia	Arthropoda	Insecta	Hymenoptera	Encyrtidae	GENUS
Sawflies	Animalia	Arthropoda	Insecta	Hymenoptera
Spheciformes	Animalia	Arthropoda	Insecta	Hymenoptera
Pompilidae	Animalia	Arthropoda	Insecta	Hymenoptera	Pompilidae	FAMILY
Chrysididae	Animalia	Arthropoda	Insecta	Hymenoptera	Chrysididae	FAMILY
Scolioidea	Animalia	Arthropoda	Insecta	Hymenoptera
Cuckoo Wasp	Animalia	Arthropoda	Insecta	Hymenoptera
Wasps	Animalia	Arthropoda	Insecta	Hymenoptera
Sphecidae	Animalia	Arthropoda	Insecta	Hymenoptera	Sphecidae	FAMILY
Wild Bees	Animalia	Arthropoda	Insecta	Hymenoptera
Scoliidae	Animalia	Arthropoda	Insecta	Hymenoptera	Scoliidae	FAMILY
Crabronidae et al.	Animalia	Arthropoda	Insecta	Hymenoptera
Chrysididae et al.	Animalia	Arthropoda	Insecta	Hymenoptera
Symphyta	Animalia	Arthropoda	Insecta	Hymenoptera
Stinging Wasps	Animalia	Arthropoda	Insecta	Hymenoptera
Mutillidae	Animalia	Arthropoda	Insecta	Hymenoptera	Mutillidae	FAMILY
Sapygidae	Animalia	Arthropoda	Insecta	Hymenoptera	Sapygidae	FAMILY
Tiphiidae	Animalia	Arthropoda	Insecta	Hymenoptera	Tiphiidae	FAMILY
Cimbicidae	Animalia	Arthropoda	Insecta	Hymenoptera	Cimbicidae	FAMILY
Siricidae	Animalia	Arthropoda	Insecta	Hymenoptera	Siricidae	FAMILY
Xiphydriidae	Animalia	Arthropoda	Insecta	Hymenoptera	Xiphydriidae	FAMILY
Ampulicidae	Animalia	Arthropoda	Insecta	Hymenoptera	Ampulicidae	FAMILY
Crabronidae	Animalia	Arthropoda	Insecta	Hymenoptera	Crabronidae	FAMILY
Apoidea	Animalia	Arthropoda	Insecta	Hymenoptera
Lepidoptera	Animalia	Arthropoda	Insecta	Lepidoptera		ORDER
Moths	Animalia	Arthropoda	Insecta	Lepidoptera
Butterflies	Animalia	Arthropoda	Insecta	Lepidoptera
Papilionoidea	Animalia	Arthropoda	Insecta	Lepidoptera
Hesperioidea	Animalia	Arthropoda	Insecta	Lepidoptera
Noctuidae	Animalia	Arthropoda	Insecta	Lepidoptera	Noctuidae	FAMILY
Night Butterflies	Animalia	Arthropoda	Insecta	Lepidoptera
Owlet Moths	Animalia	Arthropoda	Insecta	Lepidoptera
Lymantriinae	Animalia	Arthropoda	Insecta	Lepidoptera
Short-Cloaked Moth	Animalia	Arthropoda	Insecta	Lepidoptera
Geometer Moths	Animalia	Arthropoda	Insecta	Lepidoptera
Bombyces	Animalia	Arthropoda	Insecta	Lepidoptera
Sphinges S.l.	Animalia	Arthropoda	Insecta	Lepidoptera
Pyralidae	Animalia	Arthropoda	Insecta	Lepidoptera	Pyralidae	FAMILY
Sphinges	Animalia	Arthropoda	Insecta	Lepidoptera
Geometridae	Animalia	Arthropoda	Insecta	Lepidoptera	Geometridae	FAMILY
Makrolepidoptera	Animalia	Arthropoda	Insecta	Lepidoptera
Macrolepidoptera	Animalia	Arthropoda	Insecta	Lepidoptera
Microlepidoptera	Animalia	Arthropoda	Insecta	Lepidoptera
Zygaenidae	Animalia	Arthropoda	Insecta	Lepidoptera	Zygaenidae	FAMILY
Sphingidae	Animalia	Arthropoda	Insecta	Lepidoptera	Sphingidae	FAMILY
Sesiidae	Animalia	Arthropoda	Insecta	Lepidoptera	Sesiidae	FAMILY
Psychidae	Animalia	Arthropoda	Insecta	Lepidoptera	Psychidae	FAMILY
Pterophoridae	Animalia	Arthropoda	Insecta	Lepidoptera	Pterophoridae	FAMILY
Alucitidae	Animalia	Arthropoda	Insecta	Lepidoptera	Alucitidae	FAMILY
Crambidae	Animalia	Arthropoda	Insecta	Lepidoptera	Crambidae	FAMILY
Torticidae	Animalia	Arthropoda	Insecta	Lepidoptera
Choreutidae	Animalia	Arthropoda	Insecta	Lepidoptera	Choreutidae	FAMILY
Hawk Moths	Animalia	Arthropoda	Insecta	Lepidoptera
Bombycidae	Animalia	Arthropoda	Insecta	Lepidoptera	Bombycidae	FAMILY
Pantheidae	Animalia	Arthropoda	Insecta	Lepidoptera	Noctuidae	FAMILY
Nolidae	Animalia	Arthropoda	Insecta	Lepidoptera	Nolidae	FAMILY
Sessidae	Animalia	Arthropoda	Insecta	Lepidoptera
Erebidae	Animalia	Arthropoda	Insecta	Lepidoptera	Erebidae	FAMILY
Mantodea	Animalia	Arthropoda	Insecta	Mantodea		ORDER
Mecoptera	Animalia	Arthropoda	Insecta	Mecoptera		ORDER
Scorpionflies	Animalia	Arthropoda	Insecta	Mecoptera
Megaloptera	Animalia	Arthropoda	Insecta	Megaloptera
Neuroptera	Animalia	Arthropoda	Insecta	Neuroptera		ORDER
Owlflies	Animalia	Arthropoda	Insecta	Neuroptera
Net-Winged Insects	Animalia	Arthropoda	Insecta	Neuroptera
Odonata	Animalia	Arthropoda	Insecta	Odonata		ORDER
Orthoptera	Animalia	Arthropoda	Insecta	Orthoptera		ORDER
Grasshoppers	Animalia	Arthropoda	Insecta	Orthoptera
Ensifera	Animalia	Arthropoda	Insecta	Orthoptera
Caelifera	Animalia	Arthropoda	Insecta	Orthoptera
Crickets	Animalia	Arthropoda	Insecta	Orthoptera
Katydids	Animalia	Arthropoda	Insecta	Orthoptera
Stick Insects	Animalia	Arthropoda	Insecta	Phasmatodea
Stoneflies	Animalia	Arthropoda	Insecta	Plecoptera
Snakeflies	Animalia	Arthropoda	Insecta	Raphidioptera
Thrips	Animalia	Arthropoda	Insecta	Thysanoptera	Thripidae	GENUS
Trichoptera	Animalia	Arthropoda	Insecta	Trichoptera		ORDER
Caddisflies	Animalia	Arthropoda	Insecta	Trichoptera
Plecoptera	Animalia	Arthropoda	Insecta			CLASS
Insects	Animalia	Arthropoda	Insecta
Aquatic and Semi-Aquatic Bugs	Animalia	Arthropoda	Insecta
Insecta	Animalia	Arthropoda	Insecta			CLASS
Water Bugs	Animalia	Arthropoda	Insecta
Woodlice	Animalia	Arthropoda	Isopoda
Amphipoda	Animalia	Arthropoda	Malacostraca	Amphipoda		ORDER
Niphargidae	Animalia	Arthropoda	Malacostraca	Amphipoda	Niphargidae	FAMILY
Cumacea	Animalia	Arthropoda	Malacostraca	Cumacea		ORDER
Decapoda	Animalia	Arthropoda	Malacostraca	Decapoda		ORDER
Crayfishes	Animalia	Arthropoda	Malacostraca	Decapoda
Astacoidea	Animalia	Arthropoda	Malacostraca	Decapoda
Freshwater Decapod Crustaceans	Animalia	Arthropoda	Malacostraca	Decapoda
Marine Decapod Crustaceans	Animalia	Arthropoda	Malacostraca	Decapoda
Marine Isopoda	Animalia	Arthropoda	Malacostraca	Isopoda
Malacostraca	Animalia	Arthropoda	Malacostraca			CLASS
Mysidacea	Animalia	Arthropoda	Malacostraca			CLASS
Barnacles	Animalia	Arthropoda	Thecostraca
Freshwater Crabs	Animalia	Arthropoda		Decapoda
Horseshoe Crabs	Animalia	Arthropoda		Xiphosura
Crustaceans	Animalia	Arthropoda
Arthropods	Animalia	Arthropoda
Freshwater Crustaceans	Animalia	Arthropoda
Crayfish	Animalia	Arthropoda
Sea Spiders	Animalia	Arthropoda
Marine Crustaceans	Animalia	Arthropoda
Miscellaneous Arthropods	Animalia	Arthropoda
Myriapods	Animalia	Arthropoda
Brachiopods	Animalia	Brachiopoda
Marine Bryozoans	Animalia	Bryoza
Bryozoa	Animalia	Bryozoa				PHYLUM
Bony Fishes	Animalia	Chordata	Actinopterygii
Frogs	Animalia	Chordata	Amphibia	Anura
Amphibians	Animalia	Chordata	Amphibia
Sea Squirts	Animalia	Chordata	Ascidiacea
Anatidae	Animalia	Chordata	Aves	Anseriformes	Anatidae	FAMILY
Galliformes	Animalia	Chordata	Aves	Galliformes		ORDER
Birds	Animalia	Chordata	Aves
Breeding Birds	Animalia	Chordata	Aves
Wintering Birds	Animalia	Chordata	Aves
Transient Birds	Animalia	Chordata	Aves
Birds Terre Adelie	Animalia	Chordata	Aves
Birds Terres Australes	Animalia	Chordata	Aves
Birds Scattered Islands	Animalia	Chordata	Aves
Migratory Birds	Animalia	Chordata	Aves
Metropolitan Birds	Animalia	Chordata	Aves
Endemic Brids	Animalia	Chordata	Aves
Breeding Birds of Prey	Animalia	Chordata	Aves
Endangered Birds	Animalia	Chordata	Aves
Birds Ecuador	Animalia	Chordata	Aves
Birds Galapagos	Animalia	Chordata	Aves
Birds of Prey	Animalia	Chordata	Aves
Breeding Raptors	Animalia	Chordata	Aves
Freshwater Lamprey	Animalia	Chordata	Cephalaspidomorphi
Sharks	Animalia	Chordata	Chondrichthyes
Chondrichthyes	Animalia	Chordata	Chondrichthyes
Lamprey	Animalia	Chordata	Hyperoartia
Cetaceans	Animalia	Chordata	Mammalia	Artiodactyla
Bats	Animalia	Chordata	Mammalia	Chiroptera
Perissodactyla	Animalia	Chordata	Mammalia	Perissodactyla		ORDER
Primates	Animalia	Chordata	Mammalia	Primates		ORDER
Lemurs	Animalia	Chordata	Mammalia	Primates
Rodents	Animalia	Chordata	Mammalia	Rodentia
Mammals	Animalia	Chordata	Mammalia
Terrestrial Mammals	Animalia	Chordata	Mammalia
Marine Mammals	Animalia	Chordata	Mammalia
Mammals Scattered Islands	Animalia	Chordata	Mammalia
Terrestial Mammals	Animalia	Chordata	Mammalia
Aquatic Mammals	Animalia	Chordata	Mammalia
Metropolitan Mammals	Animalia	Chordata	Mammalia
Endemic Mammals	Animalia	Chordata	Mammalia
Large Mammals	Animalia	Chordata	Mammalia
Insectivores	Animalia	Chordata	Mammalia
Carnivores	Animalia	Chordata	Mammalia
Marine Cetartiodactyla	Animalia	Chordata	Mammalia
Terrestrial Cetartiodactyla	Animalia	Chordata	Mammalia
Proboscidea & Sirenia	Animalia	Chordata	Mammalia
Endangered Mammals	Animalia	Chordata	Mammalia
Ungulates	Animalia	Chordata	Mammalia
Land Mammals	Animalia	Chordata	Mammalia
Caimans	Animalia	Chordata	Reptilia	Crocodilia
Chameleons	Animalia	Chordata	Reptilia	Squamata
Lizards and Worm-Lizards	Animalia	Chordata	Reptilia	Squamata
Snakes	Animalia	Chordata	Reptilia	Squamata
Marine Turtles	Animalia	Chordata	Reptilia	Testudines
Turtles	Animalia	Chordata	Reptilia	Testudines
Sea Turtles	Animalia	Chordata	Reptilia	Testudines
Reptiles	Animalia	Chordata	Reptilia
Terrestrial Reptiles	Animalia	Chordata	Reptilia
Endemic Lizards	Animalia	Chordata	Reptilia
Endemic Reptiles	Animalia	Chordata	Reptilia
Fishes	Animalia	Chordata
Lampreys	Animalia	Chordata
Freshwater Fishes	Animalia	Chordata
Marine Fishes	Animalia	Chordata
Tunicata	Animalia	Chordata
Lancelets	Animalia	Chordata
Reef Fishes	Animalia	Chordata
Terrestrial Vertebrates	Animalia	Chordata
Freshwater and Migratory Fishes	Animalia	Chordata
Cyclostomata	Animalia	Chordata
Endangered Vertebrates	Animalia	Chordata
Endemic Freshwater Fishes	Animalia	Chordata
Linefishes	Animalia	Chordata
Brackish and Freshwater Fishes	Animalia	Chordata
Corals	Animalia	Cnidaria
Cnidaria	Animalia	Cnidaria				PHYLUM
Reef Corals	Animalia	Cnidaria
Marine Cnidaria	Animalia	Cnidaria
Echinoderms	Animalia	Echinodermata
Acorn Worms	Animalia	Hemichordata	Enteropneusta
Bivalvia	Animalia	Mollusca	Bivalvia			CLASS
Marine Bivalves	Animalia	Mollusca	Bivalvia
Mussels	Animalia	Mollusca	Bivalvia
Musslels	Animalia	Mollusca	Bivalvia
Cephalopods	Animalia	Mollusca	Cephalopoda
Gastropoda	Animalia	Mollusca	Gastropoda			CLASS
Snails	Animalia	Mollusca	Gastropoda
Marine Snails	Animalia	Mollusca	Gastropoda
Terrestrial Gastropods	Animalia	Mollusca	Gastropoda
Freshwater Gastropods	Animalia	Mollusca	Gastropoda
Mollusca	Animalia	Mollusca				PHYLUM
Molluscs	Animalia	Mollusca
Terrestrial Molluscs	Animalia	Mollusca
Non-Marine Molluscs	Animalia	Mollusca
Inland Molluscs	Animalia	Mollusca
Species-Poor Groups of Marine Molluscs	Animalia	Mollusca
Freswater Mollusc	Animalia	Mollusca
Extramarine Molluscs	Animalia	Mollusca
Nematoda	Animalia	Nematoda				PHYLUM
Ribbon Worms	Animalia	Nemertea
Flatworms	Animalia	Platyhelminthes	Turbellaria
Porifera	Animalia	Porifera				PHYLUM
Sea Sponges	Animalia	Porifera
Vertebrates	Animalia	chordata
Fauna	Animalia
Cave Fauna	Animalia
Polychaeta	Animalia					KINGDOM
Invertebrates	Animalia
Fauna_en_higher	Animalia
Fauna_nt_lc_dd	Animalia
Fauna_en_vu	Animalia
Terrestrial Invertebrates	Animalia
Aquatic Invertebrates	Animalia
Fauna_flagship Species	Animalia
Endemic Fauna	Animalia
Protected Animals	Animalia
Endangered Fauna	Animalia
Selected Species	Animalia
Marine Species	Animalia
Endangered Species	Animalia
Marine Invertebrates	Animalia
Freshwater Plants	Animalia
Flora Visiting Fauna	Animalia
Other Invertebrates	Animalia
Other Marine Invertebrates	Animalia
Marine Fauna	Animalia
Endemic Animals	Animalia
Vaucheriaceae	Chromista	Ochrophyta	Xanthophyceae	Vaucheriales	Vaucheriaceae	FAMILY
Ascomycota	Fungi	Ascomycota				PHYLUM
Agaricales	Fungi	Basidiomycota	Agaricomycetes	Agaricales		ORDER
Boletaceae	Fungi	Basidiomycota	Agaricomycetes	Boletales	Boletaceae	FAMILY
Boletales	Fungi	Basidiomycota	Agaricomycetes	Boletales		ORDER
Russulales	Fungi	Basidiomycota	Agaricomycetes	Russulales		ORDER
Ustilaginales	Fungi	Basidiomycota	Ustilaginomycetes	Ustilaginales		ORDER
Basidiomycota	Fungi	Basidiomycota				PHYLUM
Fungi	Fungi					KINGDOM
Macromycetes	Fungi
Mushrooms	Fungi
Macrofungi	Fungi
Phytoparasitic Small Fungi	Fungi
Large Mushrooms	Fungi
Lichenicolous Fungus	Fungi
Ascomycetes	Fungi
Aphyllophorales	Fungi
Phytoparasitic Microfungi	Fungi
Characeae	Plantae	Charophyta	Charophyceae	Charales	Characeae	FAMILY
Charophyceae	Plantae	Charophyta	Charophyceae			CLASS
Desmidiales	Plantae	Charophyta	Conjugatophyceae	Desmidiales		ORDER
Zygnematophyceae	Plantae	Charophyta	Zygnematophyceae			CLASS
Freshwater Diatoms	Plantae	Gyrista	Bacillariophyceae
Marchantiophyta	Plantae	Marchantiophyta				PHYLUM
Apiaceae	Plantae	Tracheophyta	Magnoliopsida	Apiales	Apiaceae	FAMILY
Cactaceae	Plantae	Tracheophyta	Magnoliopsida	Caryophyllales	Cactaceae	FAMILY
Magnoliaceae	Plantae	Tracheophyta	Magnoliopsida	Magnoliales	Magnoliaceae	FAMILY
Anisoptera	Plantae	Tracheophyta	Magnoliopsida	Malvales	Dipterocarpaceae	GENUS
Dipterocarpaceae	Plantae	Tracheophyta	Magnoliopsida	Malvales	Dipterocarpaceae	FAMILY
Magnoliophyta	Plantae	Tracheophyta				PHYLUM
Charophytes	Plantae		Charophyceae	Charales
Lycopods	Plantae		Lycopodiopsida	Lycopodiales
Tree Ferns	Plantae		Polypodiopsida
Sphagnum Mosses	Plantae		Sphagnopsida
Orchids	Plantae			Asparagales
Wild Cinnamon	Plantae			Laurales
Bromeliads	Plantae			Poales
Flora	Plantae
Bryophytes	Plantae
Ferns	Plantae
Vascular Plants	Plantae
Lichens	Plantae
Hydrophytes	Plantae
Marine Flora	Plantae
Hornworts	Plantae
Liverworts	Plantae
Mosses	Plantae
Flora Saint Paul and Amsterdam	Plantae
Flora Scattered Islands	Plantae
Flora Kerguelen	Plantae
Endemic Flora	Plantae
Trees	Plantae
Shrubs	Plantae
Algae	Plantae
Arctic Vascular Plants	Plantae
Marine Macroalgae	Plantae
Freshwater Red Algae	Plantae
Freshwater Brown Algae	Plantae
Flowering Plant	Plantae
Red Algae	Plantae
Brown Algae	Plantae
Hepaticophyta	Plantae
Broad-Leaved Mosses	Plantae
Lichen Communities	Plantae
Flora of Cerrado Biom	Plantae
Endemic Plants	Plantae
Flora On the Red List	Plantae
Protected Plants	Plantae
Endangered Plants	Plantae
Near-Endemic Flora	Plantae
Perennial Shrubs	Plantae
Flora_2	Plantae
Endemic and Range-Restricted Vascular Plantss	Plantae
Indigenous Plants	Plantae
Selected Species In Marshlands	Plantae
Conifers	Plantae
Peninsular Planrs	Plantae
Lycophytes	Plantae
Higher Plants	Plantae
Cloud Forest Trees	Plantae
Spermatophytes	Plantae
Palms	Plantae
Wild Crop Relatives	Plantae
Aquatic Plants	Plantae
Medicinal Plants	Plantae
Dry Forest Trees	Plantae
Monocotyledons	Plantae
Freshwater Flora	Plantae
Flora List	Plantae
Endemic Trees	Plantae
Myxomycetes	Protozoa	Mycetozoa	Myxomycetes			CLASS
Protozoa	Protozoa					KINGDOM
Zygoptera	Protozoa					GENUS

• The value Tracheophyta is for vascular plants. Some groups with value Flora have this but some others don’t. Is it correct?
• There are some groups with value Fauna that have Chordata and Vertebrata as phylum and subphylum, but some others don’t. Is it correct?
• The group Onychophora should be phylum Onychophora.
  
• There’s a typo on group Opilioness, should be Opiliones.
  
• Correct the value to order Pseudoscorpiones for the group False Scorpions.
  
• The group Entomostraca should not have Copepoda as class.
• Crabs should have class Malacostraca.
  
• Sharks should not have Chondrichthyes as class.
  
• Groups Cetartiodactyla and Marine Cetartiodactyla should be order Artiodactyla.
  
• Carnivores should be order Carnivora.
• Group Lizards should be order Squamata.
  
• Group Lampreys should be class Petromyzonti and order Petromyzontiformes.
• There’s a typo on group Musslels, should be Mussels.
  
• There’s a typo on the phylum chordata, should be uppercase.
• The groups Orchids and Bromeliads, are missing phylum, class and family. They should be Tracheophyta and Liliopsida. Also family Orchidaceae and Bromeliaceae.
  
• The group Wild Cinnamon, is missing phylum, class and order. They shoud be Tracheophyta Magnoliopsida Canellales Canellaceae, and the class should have family Canellaceae.
• The groups Vascular plants, Trees, Shrubs, Cloud Forest Trees, Endemic Trees, Perennial Shrubs, Arctic Vascular Plants, Spermatophytes and Angiosperms, should have Tracheophyta as phylum.
  
• The group Flowering Plant should have
• The group Ferns should have Tracheophyta and Polypodiopsida, as phylum and class.
• The group Conifers, should have class Pinopsida.
  
• The group Palms, should have Tracheophyta, Liliopsida, Arecales, and Arecaceae, as phylum, class, order and family.
• The group Monocotyledons should have Tracheophyta and Liliopsida, as phylum and class.
  
• There’s a typo in Endemic and Range-Restricted Vascular Plantss. This should have Tracheophyta as phylum.

FIX

raw_metadata %>% 
  mutate(kingdom = ifelse(group == 'Flora', 'Plantae', kingdom)) %>% 
  mutate(kingdom = ifelse(group == 'Fauna', 'Animalia', kingdom)) %>%  
  mutate(phylum = ifelse(group == 'Flora' | group == 'Fauna', NA, phylum)) %>% 
  mutate(phylum = ifelse(group =='Onychophora', 'Onychophora', phylum)) %>% 
  mutate(group = ifelse(group =='Opilioness', 'Opiliones', group)) %>% 
  mutate(group = ifelse(group =='False scorpions', 'Pseudoscorpiones', group)) %>%
  mutate(order = ifelse(group =='False scorpions', 'Pseudoscorpiones', order)) %>%
  mutate(class = ifelse(group =='Entomostraca', NA, class)) %>%
  mutate(class = ifelse(group =='Horseshoe Crabs', 'Merostomata', class)) %>%
  mutate(class = ifelse(group =='Freshwater Crabs', 'Malacostraca', class)) %>%
  mutate(class = ifelse(group =='Sharks', 'Chondrichthyes', class)) %>%
  mutate(order = ifelse(iso_2 == 'KE' & group == 'Carnivores', 'Carnivora', order),
         order = ifelse(iso_2 == 'KE' & group == 'Perissodactyla', 'Perissodactyla', order),
         order = ifelse(iso_2 == 'KE' & group == 'Marine Cetartiodactyla', 'Artiodactyla', order),
         order = ifelse(iso_2 == 'KE' & group == 'Terrestrial Cetartiodactyla', 'Artiodactyla', order)) %>%
  mutate(group = ifelse(group == 'Marine Cetartiodactyla', 'Cetaceans', group),
         group = ifelse(group == 'Terrestrial Cetartiodactyla', 'Ungulates', group)) %>% 
  mutate(order = ifelse(group =='Endemic Lizards', 'Squamata', order)) %>%
  mutate(class = ifelse(grepl('lamprey', ignore.case=T, group), 'Petromyzonti', class),
         order = ifelse(grepl('lamprey', ignore.case=T, group), 'Petromyzontiformes', order)) %>%
  mutate(group = ifelse(group == 'Musslels', 'Mussels', group)) %>% 
  mutate(phylum = ifelse(grepl('orchid', ignore.case=T, group), 'Tracheophyta', phylum),
         class = ifelse(grepl('orchid', ignore.case=T, group), 'Liliopsida', class),
         order = ifelse(grepl('orchid', ignore.case=T, group), 'Asparagales', order)) %>%
  mutate(phylum = ifelse(grepl('bromeli', ignore.case=T, group), 'Tracheophyta', phylum),
         class = ifelse(grepl('bromeli', ignore.case=T, group), 'Liliopsida', class)) %>%
  mutate(phylum = ifelse(grepl('cinnamon', ignore.case=T, group), 'Tracheophyta', phylum),
         class = ifelse(grepl('cinnamon', ignore.case=T, group), 'Magnoliopsida', class),
         order = ifelse(grepl('cinnamon', ignore.case=T, group), 'Canellales', order)) %>%
  mutate(phylum = ifelse(grepl('tree|shrub|vascular|angio|spermato|flower', ignore.case=T, group), 
                         'Tracheophyta', phylum)) %>% 
  mutate(phylum = ifelse(grepl('ferns', ignore.case=T, group), 'Tracheophyta', phylum),
         class = ifelse(grepl('ferns', ignore.case=T, group), 'Polypodiopsida', class)) %>% 
  mutate(phylum = ifelse(grepl('conif', ignore.case=T, group), 'Tracheophyta', phylum),
         class = ifelse(grepl('conif', ignore.case=T, group), 'Pinopsida', class)) %>% 
  mutate(phylum = ifelse(grepl('palm', ignore.case=T, group), 'Tracheophyta', phylum),
         class = ifelse(grepl('palm', ignore.case=T, group), 'Liliopsida', class),
         order = ifelse(grepl('palm', ignore.case=T, group), 'Arecales', order)) %>%
  mutate(class = ifelse(grepl('monocot', ignore.case=T, group), 'Tracheophyta', class)) 

Check Event fields

The fields is: year

• Check it has numeric values.

raw_metadata %>% 
  janitor::clean_names() %>% 
  janitor::remove_empty(c('rows', 'cols')) %>% 
  mutate(year = ifelse(year == 'NA', NA, year)) %>%
  mutate(year = ifelse(year == '2024?', NA, year)) %>%
  mutate(year = ifelse(name_orig == 'The Red List of Mammals of South Africa, Swaziland and Lesotho 2024', 
                       2024, year)) %>%
  mutate(year = as.numeric(year, na.rm=T)) %>% 
  filter(is.na(year)) %>% 
  select(year, name_orig) %>% 
  distinct(name_orig, .keep_all = T) 

DOUBTS

• There are 16 records with year = 2024?. Is it correct to change them for 2024? They belong to the sources:

https://www.odonat-grandest.fr/listes-rouges-grand-est-etat-avancement/
A Red List of Benin’s sharks                                           
The Red List of Mammals of South Africa, Swaziland and Lesotho 2024  

• There are 9 records without year. What should we do with them? They belong to the following source:

1 Crveni popis hrvatskih koralja                                                        
2 Crveni popis lišajeva Hrvatske                                                        
3 Červené seznamy                                                                       
4 Coleoptera (Beetle) – Invertebrate Ireland Online                                     
5 Tricoptera (Caddisfly) – InvertebrateIreland Online                                   
6 Les mammifères de la Côte d’Ivoire                                                    
7 Rongeurs et insectivores de Côte d’Ivoire, leur habitat et leur statut de conservation
8 Красная книга Азербайджанской Республики                                              
9 Красная книга Узбекистана  

Check if URLs are working

# URL incorrect
raw_metadata %>% 
  filter(!grepl('http', url_clean)) %>% 
  distinct(url_clean)

# A tibble: 3 × 1
  url_clean                                                  
  <chr>                                                      
1 NA                                                         
2 ima.sc.gov.br/index.php/biodiversidade/biodiversidade/fauna
3 <NA>                                                       

# URL error
# raw_metadata %>% filter(grepl('http', url_clean)) %>% 
#   distinct(url_clean) %>% 
#   mutate(check_URL = ifelse(map(URLencode(url_clean), http_error), 'not found', 'OK')) %>% 
#   filter(check_URL == 'not found') 

raw_metadata %>% 
  filter(!grepl('http', url_clean)) %>% 
  distinct(url_clean)

# A tibble: 3 × 1
  url_clean                                                  
  <chr>                                                      
1 NA                                                         
2 ima.sc.gov.br/index.php/biodiversidade/biodiversidade/fauna
3 <NA>                                                       

raw_metadata %>% 
  mutate(url_clean = ifelse(grepl('Nicolau', url_clean), NA, url_clean)) %>% 
  filter(!grepl('http', url_clean)) %>% 
  distinct(url_clean)

# A tibble: 3 × 1
  url_clean                                                  
  <chr>                                                      
1 NA                                                         
2 ima.sc.gov.br/index.php/biodiversidade/biodiversidade/fauna
3 <NA>                                                       

DOUBTS

• There’s one URL that resolves but has ‘http’ missing and one URL that is incorrect. Should this be NA?

ima.sc.gov.br/index.php/biodiversidade/biodiversidade/fauna
Nicolau, J. i Dalmau, J., 2008. Llista Vermella\r\ndels Vertebrats d’Andorra. BIOCOM (Biologia i\r\nComunicació) SL i Departament de Patrimoni\r\nNatural del Govern d’Andorra. Informe inèdit

Run code and keep relevant fields

metadata <- 
  # check taxon
  left_join(raw_metadata %>% mutate(group = str_trim(group)) %>%
              select(-c(kingdom,phylum,subphylum,class,order)),
            bind_rows(merged_list %>% filter(!is.na(scientificName)),
                      merged_list %>% filter(is.na(scientificName)) %>%
                        select(group) %>%
                        left_join(. , raw_metadata_taxon_list))) %>% 
  mutate(group = str_trim(group)) %>% 
  mutate(group = str_to_title(group)) %>% 
  mutate(group = str_replace_all(group, 'And ', 'and ')) %>% 
  mutate(group = str_replace_all(group, 'Of', 'of')) %>% 
  mutate(group = str_replace_all(group, 'The', 'the')) %>%
  # more taxonomic corrections
  mutate(group = str_squish(group)) %>% 
  mutate(kingdom = case_when(grepl('flora', group, ignore.case=T) ~ 'Plantae',
                             grepl('fauna', group, ignore.case=T) ~ 'Animalia',
                             .default = kingdom)) %>% 
  mutate(phylum = ifelse(phylum == 'chordata', 'Chordata', phylum)) %>% 
  mutate(kingdom = ifelse(group == 'Flora', 'Plantae', kingdom)) %>% 
  mutate(kingdom = ifelse(group == 'Fauna', 'Animalia', kingdom)) %>%  
  mutate(phylum = ifelse(group == 'Flora' | group == 'Fauna', NA, phylum)) %>% 
  mutate(phylum = ifelse(group =='Onychophora', 'Onychophora', phylum)) %>% 
  mutate(group = ifelse(group =='Opilioness', 'Opiliones', group)) %>% 
  mutate(group = ifelse(group =='False scorpions', 'Pseudoscorpiones', group)) %>%
  mutate(order = ifelse(group =='False scorpions', 'Pseudoscorpiones', order)) %>%
  mutate(class = ifelse(group =='Entomostraca', NA, class)) %>%
  mutate(class = ifelse(group =='Horseshoe Crabs', 'Merostomata', class)) %>%
  mutate(class = ifelse(group =='Freshwater Crabs', 'Malacostraca', class)) %>%
  mutate(class = ifelse(group =='Sharks', 'Chondrichthyes', class)) %>%
  mutate(order = ifelse(iso_2 == 'KE' & group == 'Carnivores', 'Carnivora', order),
         order = ifelse(iso_2 == 'KE' & group == 'Perissodactyla', 'Perissodactyla', order),
         order = ifelse(iso_2 == 'KE' & group == 'Marine Cetartiodactyla', 'Artiodactyla', order),
         order = ifelse(iso_2 == 'KE' & group == 'Terrestrial Cetartiodactyla', 'Artiodactyla', order)) %>%
  mutate(group = ifelse(group == 'Marine Cetartiodactyla', 'Cetaceans', group),
         group = ifelse(group == 'Terrestrial Cetartiodactyla', 'Ungulates', group)) %>% 
  mutate(order = ifelse(group =='Endemic Lizards', 'Squamata', order)) %>%
  mutate(class = ifelse(grepl('lamprey', ignore.case=T, group), 'Petromyzonti', class),
         order = ifelse(grepl('lamprey', ignore.case=T, group), 'Petromyzontiformes', order)) %>%
  mutate(group = ifelse(group == 'Musslels', 'Mussels', group)) %>% 
  mutate(phylum = ifelse(grepl('orchid', ignore.case=T, group), 'Tracheophyta', phylum),
         class = ifelse(grepl('orchid', ignore.case=T, group), 'Liliopsida', class),
         order = ifelse(grepl('orchid', ignore.case=T, group), 'Asparagales', order)) %>%
  mutate(phylum = ifelse(grepl('bromeli', ignore.case=T, group), 'Tracheophyta', phylum),
         class = ifelse(grepl('bromeli', ignore.case=T, group), 'Liliopsida', class)) %>%
  mutate(phylum = ifelse(grepl('cinnamon', ignore.case=T, group), 'Tracheophyta', phylum),
         class = ifelse(grepl('cinnamon', ignore.case=T, group), 'Magnoliopsida', class),
         order = ifelse(grepl('cinnamon', ignore.case=T, group), 'Canellales', order)) %>%
  mutate(phylum = ifelse(grepl('tree|shrub|vascular|angio|spermato|flower', ignore.case=T, group), 
                         'Tracheophyta', phylum)) %>% 
  mutate(phylum = ifelse(grepl('ferns', ignore.case=T, group), 'Tracheophyta', phylum),
         class = ifelse(grepl('ferns', ignore.case=T, group), 'Polypodiopsida', class)) %>% 
  mutate(phylum = ifelse(grepl('conif', ignore.case=T, group), 'Tracheophyta', phylum),
         class = ifelse(grepl('conif', ignore.case=T, group), 'Pinopsida', class)) %>% 
  mutate(phylum = ifelse(grepl('palm', ignore.case=T, group), 'Tracheophyta', phylum),
         class = ifelse(grepl('palm', ignore.case=T, group), 'Liliopsida', class),
         order = ifelse(grepl('palm', ignore.case=T, group), 'Arecales', order)) %>%
  mutate(class = ifelse(grepl('monocot', ignore.case=T, group), 'Tracheophyta', class)) %>% 
  # check columns
  janitor::clean_names() %>% 
  janitor::remove_empty(c('rows', 'cols')) %>% 
  # check source
  mutate(name_orig = str_squish(name_orig)) %>% 
  mutate(name_orig = str_remove_all(name_orig, "\\\\")) %>% 
  mutate(name_orig = str_remove_all(name_orig, "\"")) %>% 
  # missing name of source
  mutate(name_orig = case_when(grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Birds' ~ 
                                 'Liste rouge des Oiseaux du Grand Est',
                               grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Mammals' ~ 
                                 'Liste rouge des Mammifères du Grand Est',
                               grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Orthoptera' ~ 
                                 'Liste rouge des Orthoptères du Grand Est',
                               grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Fishes' ~ 
                                 'Liste rouge des Papillons de jour (Rhopalocères et Zygènes) du Grand Est',
                               grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Butterflies' ~ 
                                 'Liste rouge des Papillons de nuit du Grand Est',
                               grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Night butterflies' ~ 
                                 'Liste rouge des Hétérocères du Grand Est',
                               grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Ladybugs' ~ 
                                 'Liste rouge des Coccinelles du Grand Est',
                                grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Branchiopoda' ~ 
                                 'Liste rouge des Branchiopodes du Grand Est',
                                grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Heteroptera' ~ 
                                 'Liste rouge des Punaises du Grand Est',
                                grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Mayflies' ~ 
                                 'Liste rouge des Ephémères – Trichoptères – Plécoptères du Grand Est',
                                grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Caddisflies' ~ 
                                 'Liste rouge des Ephémères – Trichoptères – Plécoptères du Grand Est',
                                grepl('htt', name_orig) & 
                               state_province == 'Grand Est' & group == 'Stoneflies' ~ 
                                 'Liste rouge des Ephémères – Trichoptères – Plécoptères du Grand Est',
                               .default = name_orig)) %>% 
  # check format
  mutate(format = ifelse(format == 'NA', NA, str_squish(format))) %>% 
  mutate(format = ifelse(format == '?', NA, format)) %>% 
  # check language
  mutate(language = ifelse(language == 'NA', NA, str_squish(language))) %>% 
  mutate(language = str_replace(language, "/|\\|", ' | ')) %>% 
  # check location
  mutate(continent = str_squish(str_replace_all(str_squish(continent), '\\|', ' | '))) %>%
  mutate(continent = str_replace_all(continent, '_', ' ')) %>%
  mutate(continent = str_to_title(continent)) %>% 
  mutate(country = ifelse(country == 'NA', NA, str_squish(country))) %>% 
  mutate(country = str_replace_all(country, '_', ' ')) %>%
  mutate(country = ifelse(country == 'USSR', NA, str_to_title(country))) %>%
  mutate(country = str_replace_all(country, 'And ', 'and ')) %>% 
  mutate(country = str_replace_all(country, 'Of', 'of')) %>% 
  mutate(country = str_replace_all(country, 'The', 'the')) %>%
  mutate(state_province = ifelse(state_province == 'NA', NA, str_squish(state_province))) %>%
  mutate(state_province = str_to_title(state_province)) %>% 
  mutate(iso_2 = ifelse(iso_2 == 'NA' & country != 'Namibia', NA, str_squish(iso_2))) %>% 
  mutate(iso_3 = ifelse(iso_3 == 'NA', NA, str_squish(iso_3))) %>%  
  mutate(iso_2 = str_squish(str_replace_all(str_squish(iso_2), '\\|', ' | '))) %>%
  mutate(iso_3 = str_squish(str_replace_all(str_squish(iso_3), '\\|', ' | '))) %>%
  mutate(gadm_level_1 = ifelse(gadm_level_1 == 'NA', NA, str_squish(gadm_level_1))) %>%
  mutate(gadm_level_1 = str_to_title(gadm_level_1)) %>% 
  mutate(gadm_level_1 = str_replace_all(gadm_level_1, 'And ', 'and ')) %>% 
  mutate(gadm_level_1 = str_replace_all(gadm_level_1, 'Of', 'of')) %>% 
  mutate(gadm_level_1 = str_replace_all(gadm_level_1, 'The', 'the')) %>% 
  mutate(gadm_level_2 = ifelse(gadm_level_2 == 'NA', NA, str_squish(gadm_level_2))) %>%
  mutate(gadm_level_2 = str_to_title(gadm_level_2)) %>% 
  mutate(region_custom = ifelse(region_custom == 'NA', NA, str_squish(region_custom))) %>% 
  mutate(region_detail = ifelse(region_detail == 'NA', NA, str_squish(region_detail))) %>% 
  mutate(region_detail = str_squish(str_replace_all(str_squish(region_detail), '\\|', ' | '))) %>%
  # check event
  mutate(year = ifelse(year == 'NA', NA, year)) %>%
  mutate(year = ifelse(year == '2024?', NA, year)) %>%
  mutate(year = ifelse(name_orig == 'The Red List of Mammals of South Africa, Swaziland and Lesotho 2024', 
                       2024, year)) %>%
  mutate(year = as.numeric(year, na.rm=T)) %>% 
  # check urls
  mutate(url_clean = ifelse(url_clean == 'NA', NA, url_clean)) %>% 
  mutate(url_clean = ifelse(grepl('Nicolau', url_clean), NA, url_clean)) %>% 
  # select columns
  select(id, continent, 
         gadm_level_0 = country, gadm_level_1, gadm_level_2,
         region_custom, region_detail, iso_2, iso_3,
         taxa=group, kingdom, phylum, class, order, family, 
          source_name= name_orig, source_link = url_clean,
         language, year) 

metadata %>% slice_sample(n=50) %>% arrange(id) %>% 
  kableExtra::kbl(booktabs = T)  %>% 
  kableExtra::kable_styling(latex_options = c('striped', 'hold_position'))

id	continent	gadm_level_0	gadm_level_1	gadm_level_2	region_custom	region_detail	iso_2	iso_3	taxa	kingdom	phylum	class	order	family	source_name	source_link	language	year
13	Europe	Spain					ES	ESP	Bryophytes	Plantae					The 2006 Red List and an Updated Checklist of Bryophytes of the Iberian Peninsula (Portugal, Spain and Andorra)	https://www.researchgate.net/profile/Cecilia-Sergio-2/publication/259799050_The_2006_Red_List_and_an_Updated_Checklist_of_Bryophytes_of_the_Iberian_Peninsula_Portugal_Spain_and_Andorra/links/53f27b040cf2bc0c40ead6bc/The-2006-Red-List-and-an-Updated-Checklist-of-Bryophytes-of-the-Iberian-Peninsula-Portugal-Spain-and-Andorra.pdf	English	2006
373	North America	Guadeloupe					GP	GLP	Terrestrial Reptiles	Animalia	Chordata	Reptilia			Liste rouge des reptiles terrestres de la Guadeloupe (France)	https://inpn.mnhn.fr/espece/listerouge/FR/Reptiles_terrestres_Guadeloupe_2021	French	2021
383	North America	Martinique					MQ	MTQ	Odonata	Animalia	Arthropoda	Insecta	Odonata		Liste rouge des libellules de la Martinique (France)	https://inpn.mnhn.fr/espece/listerouge/FR/Odonates_Martinique_2020	French	2020
438	Europe	France	Corse				FR	FRA	Breeding Birds	Animalia	Chordata	Aves			Liste rouge des Oiseaux nicheurs de Corse	https://inpn.mnhn.fr/docs/LR_FCE/LR_regionale/Corse/LRR_oiseaux_nicheurs_amphibiens_reptiles_Corse_2018.pdf	French	2017
474	Europe	France	Île-De-France				FR	FRA	Odonata	Animalia	Arthropoda	Insecta	Odonata		Liste rouge des Libellules d'Ile-de-France	https://www.arb-idf.fr/nos-travaux/publications/liste-rouge-regionale-des-libellules-dile-de-france/	French	2014
627	Europe	Czechia					CZ	CZE	Mammals	Animalia	Chordata	Mammalia			Červený seznam savců České republiky	https://www.researchgate.net/publication/331552011_Cerveny_seznam_savcu_Ceske_republiky_The_Red_List_of_mammals_of_the_Czech_Republic_Priroda_Praha_34_155-176	Czech	2019
681	Europe	United Kingdom					GB	GBR	Orthoptera	Animalia	Arthropoda	Insecta	Orthoptera		A review of the Orthoptera (Grasshoppers and crickets) and allied species of Great Britain (NECR187)	https://publications.naturalengland.org.uk/publication/5368778738106368?category=4707656804597760	English	2015
790	Europe	Sweden			Västerbotten		SE	SWE	Flora	Plantae					SLU Artdatabanken (2020). Rödlista 2020	https://artfakta.se/sok	Swedish	2020
793	Europe	Sweden			Pite lappmark		SE	SWE	Fauna	Animalia					SLU Artdatabanken (2020). Rödlista 2020	https://artfakta.se/sok	Swedish	2020
827	Europe	Portugal					PT	PRT	Amphibians	Animalia	Chordata	Amphibia			Livro Vermelho dos Vertebrados de Portugal	https://www.icnf.pt/api/file/doc/17e63e4ea59d25ef	Portuguese	2005
849	Europe				Border zone with Moldova & Romania	Moldova | Romania	MD | RO	MDA | ROU	Fauna	Animalia					Lista rosie a speciilor de fl ora si fauna salbatica din zona de grania România – Republica Moldova	https://www.yumpu.com/it/document/view/14047287/lista-rosie-management-comun-romania#	Romanian	2008
997	Europe	Germany	Baden-Württemberg				DE	DEU	Mantodea	Animalia	Arthropoda	Insecta	Mantodea		Rote Liste und kommentiertes Verzeichnis der Heuschrecken und Fangschrecken Baden-Württembergs	https://www.lubw.baden-wuerttemberg.de/natur-und-landschaft/rote-listen	German	2019
1015	Europe	Germany	Baden-Württemberg				DE	DEU	Red Algae	Plantae					Erlauterungen zur Roten Liste der limnischen Rot- und Braunalgen Baden-Württembergs	https://www.lubw.baden-wuerttemberg.de/natur-und-landschaft/rote-listen	German	2019
1037	Europe	Germany	Bayern				DE	DEU	Cucujoidea	Animalia	Arthropoda	Insecta	Coleoptera		Rote Liste gefährdeter Cucujoidea (Coleoptera: „Clavicornia“) Bayerns	https://www.lfu.bayern.de/natur/rote_liste_tiere/2003/index.htm	German	2003
1043	Europe	Germany	Bayern				DE	DEU	Leaf Beetles	Animalia	Arthropoda	Insecta	Coleoptera		Rote Liste gefährdeter Blatt- und Samenkäfer (Coleoptera: Chrysomelidae et Bruchidae) Bayerns	https://www.lfu.bayern.de/natur/rote_liste_tiere/2003/index.htm	German	2003
1178	Europe	Germany	Brandenburg				DE	DEU	Water Beetles	Animalia	Arthropoda	Insecta	Coleoptera		Rote Liste und Artenliste der Wasserkäfer des Landes Brandenburg (Coleoptera: Hydradephaga, Hydrophiloidea part., Dryoopoidea part. und Hydraenidae)	https://lfu.brandenburg.de/lfu/de/ueber-uns/veroeffentlichungen/detail/~01-01-2000-zeitschrift-naturschutz-und-landschaftspflege-in-brandenburg-beilage-zu-heft-3-2000	German	2000
1217	Europe	Germany	Niedersachsen				DE	DEU	Vascular Plants	Plantae	Tracheophyta				Referenzliste Gefäßpflanzen	https://www.nlwkn.niedersachsen.de/artenreferenzlisten/arten-referenzlisten-198326.html	German	2021
1218	Europe	Germany	Bremen				DE	DEU	Bryophytes	Plantae					Referenzliste Moose	https://www.nlwkn.niedersachsen.de/artenreferenzlisten/arten-referenzlisten-198326.html	German	2022
1324	Europe	Germany	Hessen				DE	DEU	Amphibians	Animalia	Chordata	Amphibia			Rote Liste der Säugetiere, Reptilien und Amphibien	https://www.hlnug.de/themen/naturschutz/rote-listen	German	1996
1326	Europe	Germany	Hessen				DE	DEU	Mammals	Animalia	Chordata	Mammalia			Rote Liste der Säugetiere, Reptilien und Amphibien	https://www.hlnug.de/themen/naturschutz/rote-listen	German	1996
1428	Europe	Germany	Nordrhein-Westfalen				DE	DEU	Wasps	Animalia	Arthropoda	Insecta	Hymenoptera		Rote Liste und Gesamtartenliste der Wildbienen und Wespen - Hymenoptera - Aculeata - in Nordrhein-Westfalen	https://www.lanuk.nrw.de/themen/natur/artenschutz/rote-liste	German	2009
1431	Europe	Germany	Nordrhein-Westfalen				DE	DEU	Heteroptera	Animalia	Arthropoda	Insecta	Diptera	Sphaeroceridae	Kommentiertes Artenverzeichnis der Wanzen - Heteroptera - in Nordrhein-Westfalen	https://www.lanuk.nrw.de/themen/natur/artenschutz/rote-liste	German	2011
1454	Europe	Germany	Saarland				DE	DEU	Characeae	Plantae	Charophyta	Charophyceae	Charales	Characeae	Rote Liste und Gesamtartenliste der Armleuchteralgen (Charophyceae) des Saarlandes - 3. Fassung	https://rote-liste-saarland.de/	German	2020
1459	Europe	Germany	Saarland				DE	DEU	Reptiles	Animalia	Chordata	Reptilia			Rote Liste und Gesamtartenliste der Reptilien (Reptilia) des Saarlandes – 3. Fassung	https://rote-liste-saarland.de/	German	2020
1500	Europe	Germany	Sachsen				DE	DEU	Lichens	Plantae					Rote Liste Flechten	https://publikationen.sachsen.de/bdb/artikel/41195	German	1996
1533	Europe	Germany	Sachsen				DE	DEU	Hoverflies	Animalia	Arthropoda	Insecta	Diptera		Rote Liste Schwebfliegen	https://publikationen.sachsen.de/bdb/artikel/39280	German	1996
1626	Europe	Germany	Sachsen-Anhalt				DE	DEU	Chrysomelidae	Animalia	Arthropoda	Insecta	Coleoptera	Chrysomelidae	Rote Listen Sachsen-Anhalt 2020	https://lau.sachsen-anhalt.de/alt-vor-neuer-navigation/wir-ueber-uns-publikationen/fachpublikationen/berichte-des-lau/rote-listen-sachsen-anhalt-2048	German	2020
1631	Europe	Germany	Sachsen-Anhalt				DE	DEU	Ants	Animalia	Arthropoda	Insecta	Hymenoptera		Rote Listen Sachsen-Anhalt 2020	https://lau.sachsen-anhalt.de/alt-vor-neuer-navigation/wir-ueber-uns-publikationen/fachpublikationen/berichte-des-lau/rote-listen-sachsen-anhalt-2053	German	2020
1802	Europe	Germany	Thüringen				DE	DEU	Ptinidae	Animalia	Arthropoda	Insecta	Coleoptera	Ptinidae	Rote Liste der Aaskäfer, Nestkäfer, Poch- und Diebskäfer, Scheinbockkäfer, Düsterkäfer, Schwarzkäfer (Insecta: Coleoptera: Silphidae, Leiodidae pt., Ptinidae, Oedemeridae, Melandryidae, Tenebrionidae) und weiterer Käferfamilien Thüringens 2011	https://tlubn.thueringen.de/naturschutz/rote-listen/kaefer	German	2011
1879	Europe	Germany	Thüringen				DE	DEU	Myxomycetes	Protozoa	Mycetozoa	Myxomycetes			Rote Liste der Schleimpilze (Myxomycetes) Thüringens 2010	https://tlubn.thueringen.de/naturschutz/rote-listen/pilze	German	2010
1889	Europe	Liechtenstein					LI	LIE	Orchids	Plantae	Tracheophyta	Liliopsida	Asparagales		Orchideen des Fürstentums Liechtenstein	https://www.llv.li/serviceportal2/amtsstellen/amt-fuer-umwelt/publikationen/naturkindliche-forschung/b13-orchideen.pdf	German	2000
1942	South America	Brazil	Paraná				BR	BRA	Flora	Plantae					Lista de Ameaça de Flora e Fauna do Estado do Paraná. Última avaliação da Flora (2008) e da Fauna (2010).	https://specieslist.sibbr.gov.br/speciesListItem/list/drt1572895366472	Portuguese	2008
2078	Africa	Tanzania					TZ	TZA	Endemic Reptiles	Animalia	Chordata	Reptilia			Tanzania's reptile biodiversity: Distribution, threats and climate change vulnerability	https://www.researchgate.net/publication/303288672_Tanzania%27s_reptile_biodiversity_Distribution_threats_and_climate_change_vulnerability	English	2016
2154	Asia	Iraq					IQ	IRQ	Selected Species In Marshlands	Plantae					Regional red list assessment of selected species in the Iraqi marshlands	https://www.academia.edu/42250427/Regional_red_list_assessment_of_selected_species_in_the_Iraqi_marshlands	English	2013
2155	Asia	Viet Nam					VN	VNM	Flora	Plantae					Vietnam Red Data Book. Part II. Plants	http://vuonquocgiachumomray.vn/upload/104881/20221227/SACHDOVN-PHANII_9924e.pdf	Vietnamese	2007
2158	Asia	Viet Nam					VN	VNM	Flora	Plantae					Red Data Book 2004 of Vietnam	https://www.researchgate.net/publication/285304215_Red_Data_Book_2004_of_Vietnam	Vietnamese	2004
2255	Oceania	Australia	Northern Territory				AU	AUS	Flora	Plantae					Commonwealth, State and Territory listed threatened species report	https://www.environment.gov.au/sprat-public/action/report	English	2014
2297	Europe				Carpathians	Austria | Czechia | Poland | Slovakia | Ukraine | Romania	AT | CZ | PL | SK | UA | RO	AUT | CZE | POL | SVK | UKR | ROU	Vascular Plants	Plantae	Tracheophyta				Carpathian List of Endangered Species	https://archive.nationalredlist.org/files/2012/08/Carpathian-List-of-Endangered-Species-2003.pdf	English	2003
2519	Europe	Poland	Silesian				PL	POL	Odonata	Animalia	Arthropoda	Insecta	Odonata		Czerwone listy wybranych grup zwierząt bezkręgowych	https://www.sbc.org.pl/dlibra/publication/180509	Polish	2012
2689	South America	Argentina			Tandilia Mountains		AR	ARG	Reptiles	Animalia	Chordata	Reptilia			An annotated list of the reptiles of the highland grassland of Tandilia Mountains, Argentina	https://ri.conicet.gov.ar/handle/11336/139060	English	2021
2851	Asia	Bangladesh					BD	BGD	Mammals	Animalia	Chordata	Mammalia			Red List of Threatened Animals of Bangladesh	https://portals.iucn.org/library/node/7786	English	2000
2912	Asia	Japan					JP	JPN	Reptiles	Animalia	Chordata	Reptilia			レッドリスト2018	https://ikilog.biodic.go.jp/Rdb/booklist	Japanese	2018
2925	Asia	Japan					JP	JPN	Crustaceans	Animalia	Arthropoda				海洋生物レッドリスト（2017)	https://ikilog.biodic.go.jp/Rdb/booklist	Japanese	2017
2954	Asia	Japan					JP	JPN	Mammals	Animalia	Chordata	Mammalia			レッドリスト2012	https://ikilog.biodic.go.jp/Rdb/booklist	Japanese	2012
3086	Asia	Russia	Dagestan				RU	RUS	Flora	Plantae					Красная книга Республики Дагестан. Редкие, находящиеся под угрозой исчезновения виды животных и растений. Махачкала, 1998.	https://www.plantarium.ru/lang/en/page/redbook/id/233.html	Russian	1998
3102	Asia	Russia	Zabaykal'ye				RU	RUS	Flora	Plantae					Перечень объектов растительного мира, занесенных в Красную книгу Забайкальского края (в редакции постановления Правительства Забайкальского края от 04 февраля 2014 года № 20).	https://www.plantarium.ru/lang/en/page/redbook/id/231.html	Russian	2014
3148	Europe	Russia	Krasnodar				RU	RUS	Fauna	Animalia					Красная книга Краснодарского края	https://www.cerambyx.uochb.cz/assets/pdf/collective_2017_krasnaya_kniga_zhivotnie.pdf	Russian	2017
3174	Europe	Russia	Moscow City				RU	RUS	Flora	Plantae					Список редких, находящихся под угрозой исчезновения и уязвимых в условиях города Москвы видов животных и растений, занесенных в Красную книгу города Москвы. Приложение 2 к постановлению Правительства Москвы от 2 июля 2019 г. № 745-ПП	https://www.plantarium.ru/page/redbook/id/289.html	Russian	2019
3241	Europe	Russia	Tambov				RU	RUS	Flora	Plantae					Красная книга Тамбовской области: Растения, лишайники, грибы. Тамбов, 2002.	https://www.plantarium.ru/lang/en/page/redbook/id/236.html	Russian	2002
3268	Europe	Russia	Ul'yanovsk				RU	RUS	Flora	Plantae					Красная книга Ульяновской области. Москва, 2015.	https://www.plantarium.ru/lang/en/page/redbook/id/240.html	Russian	2015

Data audit

Summary

metadata %>% 
  summarise(`Number of records` = n(),
            `Number of sources` = n_distinct(source_name),
            `Number of taxa` = n_distinct(taxa),
            `Animalia records` = sum(kingdom == 'Animalia'),
            `Plantae records` = sum(kingdom == 'Plantae'),
            `Fungi records` = sum(kingdom == 'Fungi'),
            Countries = n_distinct(gadm_level_0),
            `Sub-national territories` = n_distinct(gadm_level_1)) %>% 
  t() %>% `colnames<-`(c("N")) %>% 
  kableExtra::kbl(booktabs = T)  %>% 
  kableExtra::kable_styling(latex_options = c('striped', 'hold_position'))

	N
Number of records	3200
Number of sources	2091
Number of taxa	483
Animalia records	2189
Plantae records	899
Fungi records	105
Countries	170
Sub-national territories	223

Geographic coverage

metadata %>%
  separate_rows(continent, sep = '\\|') %>%
  mutate(continent = str_squish(continent)) %>%
  group_by(continent) %>% 
  summarise(n_sources = ifelse(n_distinct(source_name, na.rm = TRUE)==0, 
                               0, n_distinct(source_name, na.rm = TRUE))) %>% 
  arrange(desc(n_sources)) %>% 
  rename(`Number of sources`=n_sources) %>% 
  adorn_totals('row') %>% 
  kableExtra::kbl(booktabs = T)  %>% 
  kableExtra::kable_styling(latex_options = c('striped', 'hold_position')) 

continent	Number of sources
Europe	1439
Asia	272
Africa	143
South America	116
North America	112
Oceania	31
Antarctica	7
Total	2120

metadata %>%
  separate_rows(continent, sep = '\\|') %>%
  mutate(continent = str_squish(continent)) %>%
  group_by(continent) %>% 
  summarise(n_records = n()) %>% 
  arrange(desc(n_records)) %>% 
  rename(`Number of records`=n_records) %>% 
  adorn_totals('row') %>% 
  kableExtra::kbl(booktabs = T)  %>% 
  kableExtra::kable_styling(latex_options = c('striped', 'hold_position')) 

continent	Number of records
Europe	2182
Asia	470
Africa	205
North America	156
South America	150
Oceania	62
Antarctica	7
Total	3232

# Europe
metadata %>%
  separate_rows(continent, sep = '\\|') %>%
  mutate(continent = str_squish(continent)) %>%
  filter(continent == 'Europe') %>% 
  group_by(kingdom) %>% 
  summarise(n_records = n()) %>% 
  arrange(desc(n_records)) %>% 
  rename(`Number of records`=n_records) %>% 
  adorn_totals('row') %>% 
  kableExtra::kbl(booktabs = T)  %>% 
  kableExtra::kable_styling(latex_options = c('striped', 'hold_position')) 

kingdom	Number of records
Animalia	1551
Plantae	549
Fungi	75
Protozoa	6
Chromista	1
Total	2182

metadata %>%
  separate_rows(continent, sep = '\\|') %>%
  mutate(continent = str_squish(continent)) %>%
  filter(continent == 'Asia') %>% 
  group_by(kingdom) %>% 
  summarise(n_records = n()) %>% 
  arrange(desc(n_records)) %>% 
  rename(`Number of records`=n_records) %>% 
  adorn_totals('row') %>% 
  kableExtra::kbl(booktabs = T)  %>% 
  kableExtra::kable_styling(latex_options = c('striped', 'hold_position'))

kingdom	Number of records
Animalia	277
Plantae	175
Fungi	18
Total	470

world <- geodata::world(resolution = 3, level = 0, path = 'data/')

world_records <- left_join(st_as_sf(world), metadata %>%
  separate_rows(iso_3, sep = '\\|') %>% 
  select(iso_2, GID_0=iso_3, source_name)) %>% 
  group_by(GID_0, NAME_0) %>% 
  summarise(n_sources = ifelse(n_distinct(source_name, na.rm = TRUE)==0, 
                               0, n_distinct(source_name, na.rm = TRUE)),
            iso_2_string = ifelse(n_sources>0,
                                  paste(iso_2, collapse = ';'), NA)) %>% 
  ungroup() %>% st_cast() %>% st_set_crs(4326)

plot_figure_1 <- tm_shape(world_records %>% 
           select(-iso_2_string) %>% 
           mutate(n_sources=ifelse(n_sources==0,
                                   NA, n_sources))) +
  tm_polygons(fill = 'n_sources',fill_alpha = 0.9,
              col='grey40', col_alpha = 0.2,
              fill.scale = tm_scale_intervals(n = 6, 
                                              #style = 'jenks', 
                                              breaks = c(1,5,10,20,100,979),
                                              values = 'brewer.reds',
                                              value.na = 'grey80',
                                              label.na = '0'),
              fill.legend = tm_legend(item.space = 0, item.na.space = 0,
                                      title = 'Number of sources', 
                                      reverse=T, 
                                      # frame=F, 
                                      frame.lwd = 0.1,
                                      bg.color = 'white')) +
  tm_layout(legend.outside = T, 
            legend.position = c('left','bottom'),  frame=F) +
  tm_crs(property='global')

tmap_mode('plot')
plot_figure_1

tmap_mode('view')
plot_figure_1

Taxonomic coverage

# by kingdom
metadata %>%
  group_by(kingdom) %>% 
  summarise(n_records = n()) %>% 
  arrange(desc(n_records)) %>% 
  rename(`Number of records` = n_records) %>% 
  adorn_totals('row') %>% 
  kableExtra::kbl(booktabs = T)  %>% 
  kableExtra::kable_styling(latex_options = c('striped', 'hold_position'))

kingdom	Number of records
Animalia	2189
Plantae	899
Fungi	105
Protozoa	6
Chromista	1
Total	3200

# animalia
metadata %>% 
    filter(!is.na(class)) %>% 
    filter(kingdom %in% c('Animalia')) %>% 
    group_by(kingdom, class) %>% 
    summarise(n_sources_taxa = n()) %>% 
    arrange(desc(n_sources_taxa)) %>% 
    slice_head(n=5) %>% 
  rename(`Number of sources` = n_sources_taxa) %>% 
  adorn_totals('row') %>% 
  kableExtra::kbl(booktabs = T)  %>% 
  kableExtra::kable_styling(latex_options = c('striped', 'hold_position'))

kingdom	class	Number of sources
Animalia	Insecta	854
Animalia	Mammalia	202
Animalia	Aves	185
Animalia	Reptilia	148
Animalia	Amphibia	133
Total	-	1522

# plantae
metadata %>% 
    filter(!is.na(order)) %>% 
    filter(kingdom %in% c('Plantae')) %>% 
    group_by(kingdom, order) %>% 
    summarise(n_sources_taxa = n_distinct(source_name)) %>% 
    arrange(desc(n_sources_taxa)) %>% 
    slice_head(n=5) %>% 
  rename(`Number of sources` = n_sources_taxa) %>% 
  kableExtra::kbl(booktabs = T)  %>% 
  kableExtra::kable_styling(latex_options = c('striped', 'hold_position'))

kingdom	order	Number of sources
Plantae	Charales	18
Plantae	Asparagales	5
Plantae	Arecales	4
Plantae	Caryophyllales	2
Plantae	Malvales	2

# fish sources
metadata %>% 
  filter(grepl('fish', taxa, ignore.case=T)) %>% 
  filter(!grepl('crayfish', taxa, ignore.case=T)) %>% 
  distinct(source_name) %>% count() %>% 
  rename(`Number of fish sources` = n) %>% 
  kableExtra::kbl(booktabs = T)  %>% 
  kableExtra::kable_styling(latex_options = c('striped', 'hold_position'))

Number of fish sources
102

metadata %>% 
  filter(!is.na(phylum)) %>% 
  filter(kingdom %in% c('Animalia')) %>% 
  group_by(kingdom, phylum) %>% 
  summarise(n_sources_taxa = n_distinct(source_name)) %>% 
  ggplot(aes(x = reorder(phylum, -n_sources_taxa), y = n_sources_taxa)) +
  geom_bar(stat = "identity", fill = "#4CAF50") +
  facet_wrap(~kingdom, scales = 'free') +
  labs(x = "", y = "Number of sources") +
  ggpubr::theme_pubclean() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) 

metadata %>% 
  filter(!is.na(phylum)) %>% 
  filter(kingdom %in% c('Plantae', 'Fungi')) %>% 
  group_by(kingdom, phylum) %>% 
  summarise(n_sources_taxa = n_distinct(source_name)) %>% 
  ggplot(aes(x = reorder(phylum, -n_sources_taxa), y = n_sources_taxa)) +
  geom_bar(stat = "identity", fill = "#4CAF50") +
  facet_wrap(~kingdom, scales = 'free') +
  labs(x = "", y = "Number of sources") +
  ggpubr::theme_pubclean() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) 

metadata %>% 
  filter(!is.na(order)) %>% 
  filter(kingdom %in% c('Animalia')) %>% 
  group_by(kingdom, order) %>% 
  summarise(n_sources_taxa = n_distinct(source_name)) %>% 
  ggplot(aes(x = reorder(order, -n_sources_taxa), y = n_sources_taxa)) +
  geom_bar(stat = "identity", fill = "#4CAF50") +
  facet_wrap(~kingdom, scales = 'free') +
  labs(x = "", y = "Number of sources") +
  ggpubr::theme_pubclean() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) 

metadata %>% 
  filter(!is.na(order)) %>% 
  filter(kingdom %in% c('Plantae', 'Fungi')) %>% 
  group_by(kingdom, order) %>% 
  summarise(n_sources_taxa = n_distinct(source_name)) %>% 
  ggplot(aes(x = reorder(order, -n_sources_taxa), y = n_sources_taxa)) +
  geom_bar(stat = "identity", fill = "#4CAF50") +
  facet_wrap(~kingdom, scales = 'free') +
  labs(x = "", y = "Number of sources") +
  ggpubr::theme_pubclean() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) 

Temporal coverage

metadata %>% 
  group_by(year) %>% 
  summarise(publications_year = n_distinct(source_name)) %>% 
  ggplot(aes(x = year, y = publications_year)) +
  geom_bar(stat = "identity", fill = "#4CAF50") +
  ylim(c(0,150)) + xlim(c(1975,2025)) +
  scale_x_continuous(n.breaks = 15) +
  labs(x = "", y = "Number of sources") +
  ggpubr::theme_pubclean()

Save file

write_csv(metadata, 'data/metadata.csv', na = '')